Mining Reusable Software Components from Object-Oriented Source Code using Discrete PSO and Modeling Them as Java Beans

Rathee, Amit; Chhabra, Jitender Kumar

doi:10.1007/s10796-019-09948-4

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…4. The logical components are transformed into the corresponding reusable physical component by following recommendations of a Java Beans component model [6].…”

Section: Methodsmentioning

confidence: 99%

Extraction and Evaluation of Software Components from Object-Oriented Artifacts

Rathee

Chhabra²

2021

IJCAI

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“…4. The logical components are transformed into the corresponding reusable physical component by following recommendations of a Java Beans component model [6].…”

Section: Methodsmentioning

confidence: 99%

Extraction and Evaluation of Software Components from Object-Oriented Artifacts

Rathee

Chhabra²

2021

IJCAI

Self Cite

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“…As an example of this approach, [4] proposed an architecture recovery approach called ROMANTIC, helped by human experts. Similarly, using particle swarm optimization, [5] proposed a method that uses a meta-heuristic search-based clustering. Frequent usage patterns propelled instead the search heuristics in [6].…”

Section: Introductionmentioning

confidence: 99%

Using Structural and Semantic Information to Identify Software Components

Sas¹,

Capiluppi²

2021

Preprint

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Component Based Software Engineering (CBSE) seeks to promote the reuse of software by using existing software modules into the development process. However, the availability of such a reusable component is not immediate and is costly and time consuming. As an alternative, the extraction from preexisting OO software can be considered.In this work, we evaluate two community detection algorithms for the task of software components identification. Considering 'components' as 'communities', the aim is to evaluate how independent, yet cohesive, the components are when extracted by structurally informed algorithms.We analyze 412 Java systems and evaluate the cohesion of the extracted communities using four document representation techniques. The evaluation aims to find which algorithm extracts the most semantically cohesive, yet separated communities.The results show a good performance in both algorithms, however, each has its own strengths. Leiden extracts less cohesive, but better separated, and better clustered components that depend more on similar ones. Infomap, on the other side, creates more cohesive, slightly overlapping clusters that are less likely to depend on other semantically similar components.

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An Automated Approach for Mapping Between Software Requirements and Design Items: An Industrial Case from Turkey

Karagöz

Tosun

2022

Geoinformatics and Data Analysis

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Mining Reusable Software Components from Object-Oriented Source Code using Discrete PSO and Modeling Them as Java Beans

Cited by 9 publications

References 30 publications

Extraction and Evaluation of Software Components from Object-Oriented Artifacts

Extraction and Evaluation of Software Components from Object-Oriented Artifacts

Using Structural and Semantic Information to Identify Software Components

An Automated Approach for Mapping Between Software Requirements and Design Items: An Industrial Case from Turkey

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